Cities are expanding and more and more citizens are exposed to air pollutants both indoors and outdoors. This may have adverse effects on citizens' health. In this paper, we present AirSense, an opportunistic crowd-sensing based air quality monitoring system, aimed at collecting and aggregating sensor data to monitor air pollution in the vicinity (building/neighbourhood) and the city. We introduce a light weight, low power and low cost air quality monitoring device (AQMD) and demonstrate how AQMD and smartphones in a crowd collaboratively gather and share data of interest to the cloud. In cloud, collected data are analyzed and an aggregate view is generated from data collected from various sensors and from different users for providing an air pollution heat map of the city. Unlike previous works, both micro and macro level air quality monitoring is possible with Airsense. End user can view his/her pollution footprint for the whole day, the neighborhood (local) air quality and AQImap (air quality index map) of the city on his/her smartphone. The system is implemented and the prototype is also evaluated.

Citizens are exposed to air pollutants both indoors and outdoors due to their activities, which may result in a variety of health effects. In this paper, we present AirSense, an opportunistic crowd-sensing based air quality monitoring system, aimed at collecting and aggregating sensor data to monitor air pollution in the vicinity and the city. We introduce a light weight, low power and low cost air quality monitoring device (AQMD) and demonstrate how AQMD and smart phone devices in a crowd collaboratively (through offloading) gather and share data of interest to the cloud. In cloud, collected data will be analyzed and an aggregated view will be generated for providing an air pollution heat map of the city. End user can view both the neighborhood (local) air quality and AQImap (air quality index map) of the city on his/her smartphone.

Here, we present a prototype of a smart building using newly surfacing technologies like IoT (Internet of Things), fog and cloud for the smart city. The demand for everything smart is increasing daily, but the main stumbling block is its high price. So, our aim is to improve the standard of living in home and in office with newly improved working facilities where the whole system will be automatic, efficient and will be under the control of the user via his/her smartphone or computer but the cost will stay within the budget of a common man. All these are done by the incorporation of IoT, fog and cloud. The assimilation is done using open source hardwares and softwares to reduce the cost dramatically than the other existing solutions and implement it in an impressive and ingenious way without compromising QoS (Quality of Service) of any of the functionalities provided by other existing solutions.

The integration of Artificial Intelligence (AI) into sixth-generation (6G) networks is a foundational requirement for achieving unprecedented performance, but it also introduces a sophisticated threat landscape that legacy security frameworks cannot address. This paper presents a comprehensive review of this dual role of AI, analyzing its potential to both compromise and safeguard future networks. Since AI has the ability to both protect and compromise security and privacy, its implementation with 6G technology may sometimes be a double-edged sword. The primary objective of this survey is to systematically analyze existing research that integrates AI techniques into 6G architectures, focusing on their implications for security and privacy. Among the concerns being investigated is the fundamental privacy and security risk associated with 6G technologies. Therefore, in order to incorporate and confirm this foundational research as a platform for future research, we have developed a review on the specifics of 6G security and privacy. The methodology involves reviewing recent academic and industrial studies related to AI-enabled 6G frameworks, threat models, and defense mechanisms, with an emphasis on how AI contributes to intrusion detection, authentication, and privacy preservation. This paper begins with a historical analysis of previous networking technologies and how they impacted contemporary 6G networking improvements. Therefore, this article discusses extensively the aspects that have rendered 6G technology relevant as well as the ongoing 6G-based projects. In addition, it identifies and critically evaluates key enabling technologies, including distributed ledger technology (DLT/blockchain), physical layer security (PLS), terahertz (THz) communication, quantum computing, visible light communication (VLC), and distributed AI/ML, that underpin secure 6G environments. The paper concludes by summarizing open challenges, future research opportunities, and potential pathways for building trustworthy AI-driven 6G systems.